Creating the Engine for Scientific Discovery:
Nobel Turing Challenge as a grand challenge
project in AI and Systems Biology

Hiroaki Kitano

Photo by Hiroaki Kitano, 2002

By 2050, develop AI systems that can makemajor scientific discoveries that worth
Nobel Prize in Physiology and Medicine

Photo by Hiroaki Kitano, 2002

AI Magazine, Spring 2016

人工知能学会誌

２０１６年３月号

日経エレクトロニクス

Nobelʼs Will

The said interest shall be divided into
five equal parts, which shall beapportioned as follows: one part tothe person who shall have made themost important discovery or invention
within the field of physics; one part tothe person who shall have made themost important chemical discovery or
improvement; one part to the person
who shall have made the mostimportant discovery within the domainof physiology or medicine;Nobel Turing Challenge
The Nobel Committee to give AI system
the Nobel Prize without noticing it is an
AI system, not a human scientist.
The Turing Test at the Nobelquality scientific activities
Alfred Nobel

Alan TuringNicolas Bourbaki

Satoshi Nakamoto

Two sub-goals
First, we will need to develop an AI and robotics system that can performbiomedical and biotechnology research fully autonomously that leads to major
discoveries.
Second, the machine must be able to make strategic choice of the topic ofresearch, communicate in form of publications and other means to explain thevalue, methods, reasonings behind the discovery, and their applications andsocial implications.

An Introspective Review of Scientific Discovery
in Biomedical Sciences
Case Study: Prediction of Aging Mechanisms

1994 - 1998

Prof. Shin-ichirou Imai
Washington Univ. St. Louis

Kitano and Imai, J. Exp. Gerontology, 1998

Generating Hypotheses on Mechanisms
Molecular Mechanisms
Accumulationof molecules

Cellular Mechanisms

Telomere
Shortening

Autocrine
Cellautonomous

Heterochromatin
Re-modelling

RandomMutations
ParacrineIdentification of a model that explains the data set

Kitano and Imai, J. Exp.
Gerontology, 1998

The only model that explained the data set
CounterCounterMath
Effector
Genes

Biology

Effector
Genes

S-Process
Genes

Paracrine
Cell Arrest

C-Process
Genes

Autonomous
Cell Arrest

Structure of Biological Discovery
Explaining mechanisms of biological phenomena
Experimental Constraints
Empirical Constraints

Possible combinations ofmolecular mechanisms

Biochemical Constraints
Systems Constraints

Molecules involved

Inferring Sir2 to be the critical factor for aging andpredicting the role of Sir2 in aging and verifying it
1. Implicated in Yeast aging
2. Histone Modifier
3. Conserved among species
Budding
Yeast
Sir2

Experimentally verified

Imai, S., et al., Nature 403(6771):
795-800, 2000

Kitano, H. and Imai, S., “The two-process model of cellular aging,”
Exp. Gerontol, Aug; 33(5):393-419, 1998
Imai, S. and Kitano, H., “Heterochromatin islands and their dynamic
reorganization: a hypothesis for three distinctive features of cellular
aging,” Exp. Gerontol, Sep; 33(6):555-70. 1998

Sirtuins Family

Life-span extension by SIRT1
over-expressing mouse

17

The process of discovery
1994

Problem definition
Hypotheses on molecularmechanisms

Hypothesis
Generation

Computational modeling
1998

Biological interpretation

2000-2015 Experimental verification
2016

Constraint-based
hypotheses
generation

Experimental design,
execution, etc.

Clinical trials
18

Scientific discovery is at preindustry revolution level

19

Photo by Hiroaki Kitano, 2010

Process of Scientific Discovery

Serendipity
By Accident
Scientific Intuition

20

Systems Biology is Science
for AI
or AI-Human Hybrid System

21

Cognitive Problems in Scientific Discovery

1.Information Horizon Problem
2.Information Gap Problem
3.Phenotyping Inaccuracy Problem
4.Cognitive Bias Problem
5.Minority Report Problem

Papers are knolwledge
1.5Million papers / Year= 4100 papers/ dayYeast Signaling24

Yeast Cell Cycle

25

26

Oda and Kitano, Molecular Systems Biology, 2006

50

28
Matsuoka, et al., Weaving Knowledge of Biochemical Pathway in collaboration,
to appear in Computational Systems Toxicology• 99% of reports indicate “A activates B”• 1% of reports indicate “A inhibits B”(A) Ignore Minority Report?

(B) Examine quality of Minority Report(B-1) All reports are from one lab.

(B-2) Reports are from diverse labs

Kitano, H., AI Magazine, March 2016 (to appear)Human Cognitive Bias andLimitations of Semantic Mapping
Cognitive Bias inClinical Reasoning• Anchoring Bias
• Availability Bias
• Confirmation Bias
• Premature Closure• Representativeness

Map is not
the territory

Alfred Korzybski

30Limits of Human
Cognition

31

Power of Computation
Power of AI

Orignal Image

Image of Noise (amplified)Free Image（https://pixabay.com/）

Deep Learning identified
as Ostrich

Szegedy, et al., “Intriguing
properties of neural networks,”
International Conference on
Learning Representation, 2014

Ruderfer, D. and Dudley, J., Nature Biotech, 34 (8) 2016

Alexandrov, V., et al.,Nature Biotech, 34 (8) 2016

Non-Pharmacological Intervention for Dementia
Aikomi Dashboard

Aikomi Analytics

Aikomi database

Aikomimonitoringalways on

microphone
accelerometer

triggeredinfra-red

AikomiStimulation
screenspeakercameravapourizer

mw radarbehavioural biomarker

non-pharmacological therapy

DTOX: Deep neural network based
computational framework to analyze
omics data in TOXicology

Percellome “Per Cell” -ome Database
Oral, Inhalation

Chemicals

mR
m
Numb
（Ab ）
NA
erR
Per
sol
Cop
N
Cell
yute
A
0

３
２
oDs １
eoDs 0
eo s
e(

2
8
4
4
h
2
h
r
h
r
r

Per One Chemical,
4 time points （2hr, 4hr,
8hr, 24hr),
4 dose levels (0, 1, 2, 3)
16 groups n=3
48 mice
Liver (Lung, Kidney, brain,etc)

Affymetrix GeneChip
MOE43045,000 probe set dataDv)

One chemcial
data consists of
45,000 layers of
3D surface

Open Access : BMC Genomics. 2006 Mar 29;7(1):64

Akr1a1

Akr1c20

Aox1

Cat

Cbr1

Ephx1

Gclc

Gclm

Gsr

Gm3776 ///Gsta1 ///
Gsta2

Gsta2

Gsta3

Gsta4

Gstm1

Gstm2

Gstm3

Keap1

Nqo1

LOC1008620
12 /// Prdx1

Psma1

Psmb8

Rassf1

Atxn10

Txn1

Txnip

Txnip
(Inverted)

Txnip

Txnip
(Inverted)

Txnrd1

Gm1821 ///Ubb

J Tox Sci, Vol. 38-4, 2013

Examples of Percellome Data

40

DTOX: Deep neural network based computationalframework to analyze omics data in TOXicology
Training data

Class balanced
Gray scaled/resized
Gene 1
Gene 1
Gene 2

Deep convolutional
neural network

Gene 2

Gene 3
Open source
computer vision
softwrare library

Image size:
321 x 321

Gene 3
Image size:
32 x 32

Test data
Positive:
1,148 images
Negative:
95 images
Rough:
124 images
Intermediate: 125 images

Resnet (https://arxiv.org/abs/1512.03385)
We used network composed of
18 convolutional layers and thatof 34 convolutional layers

Classifier model

The model classify surface images
into 4 classes, positive, negative,
rough, and intermediate classes

Performanceof the model(Accuracy)
42

98%

Creating the Engine for
Scientific Discovery

DENDRAL (1969)
Joshua Lederberg, Bruce Buchanan & Ed Feigenbaum

META-DENDRALHypothesis
HEURISTIC-DENDRAL
Evaluation
DENDRAL TeamKing, et al., Science 324, 85 (2009)46

Redefining Scientific Discovery
Massive search and verification of
hypotheses space

How efficiently can we execute?
What are science specific constraints?
What is computational definition of “Serendipity”?
47

SEARCH and OPTIMIZATIONACCIDENT, SEARCH and
OPTIMIZATION

Goal: Reprogram Cell to gain Stemness

Accidental discovery of thin film formationin polyacetylene polymerization process

SEARCH

Search 24 genes
from FANTOM
DB

24 genes will enable reprogramming

OPTIMIZATION

Leave-one-out
experiments

Yamanaka Factors identified
Nobel Prize in Physiology andMedicine 2012

ACCIDENT

Goal: Polyacetylene thin film formation condition
OPTIMIZATION

Search optimal thin film
formation condition

Prof. Alan MacDiarmid

Goal: Conducting polymer
SEARCH &OPTIMIZATION

Prof. Alan Heeger

Conducting polymer thin film
Nobel Prize in Chemistry 2000

Technology Platform
Autonomous AI Scientist

Autonomous AI

Integrated AI Module

Integrated AI

Basic AI Modules

Task Specific AI

Basic Functional Modules

Connectivity &Automation

Intelligence/
Autonomy

Strategic Path
Nobel Turing Challenge
Target Zone

AI Assistant for
Scientific Discovery

Current Status

Connected ResearchLaboratories

Task CoverageTechnology Platform (SBI/SBX)
Domains

BioMedicine

BioEngineering

Clinical

Health-care

Drug
Discovery

BeyondBiology・・・

Custom Analytics and Systems Integration Services

Connectivity/Automation Framework
GarudaVisualization

Data
DeviceSensorDB

AI Framework
Gandhara

Analytic
Tool A
Analysis

Visualizer
A

Analytic
Tool B

Visualizer
B

Analytic
Tool CGaruda Platform

Visualizer
Visualizat
C

ion

Deep
Learning

MachineLearning

Image
analysis

Classification

Decisionanalysis

InferenceCloud

Knowledge
Cloud

Text
Mining

Garuda & Connected Devices
Connect to diverse spectrum of sensors and devices
The Garuda Platform provides the capability to build connect
digital solutions by integrating off-the-shelf wearables andmonitoring devices
Smart Phone cameras

Compliance detection

Face Recognition
Mood Monitoring

Game Therapy
Wireless
communications

Alert System
Connected
GarudaPlatform

Motion Sensors

Behavior
patterns
Movement patterns

Efficacy monitors

RemindersTremor sensors

Physiology Sensors
Motion sensor on pill-boxes

Behavioral
Analytics

Wearables andSensors

52

PhysiologicalModeling
Model DB

Organ-Scale
Modeling
Medical Imaging
High Vesnarinone

No Vesnarinone

Cellular Modeling

Genomic Data

Spiral wave

Ion-channel
Modeling

Regularpropagation

Shimadzu Collaboration Project

VizCloud*
Metabolites Information
Query

Explore KEGG pathway
for KEGG metabolite IDs
Shimadzu Data Explorer
Gadget receives andShimadzu Data Explorer (Proto)
processes themetabolomics data (szf)file from the Machine

MetabolicPathway
Enrichment in
Reactome

Gene Annotator*

iPath

Reactome Pathways
Load experimental
data and visualize

Case Studies

VANTED54

Multi-Omics data analysis configuration
Metabolome

Automated datavisualization

Data analysis byvarious Gadgets

Metabolite pool size
LC/MS/MS

Proteome
Enzyme abundancenanoLC/MS/MS

ShimadzuMSData
Import gadget

FluxomeMetabolic flux
GC/MS

DataMapping
GadgetVANTED

Blank map file

Data files

Merge the multi-dimensional data for downstream analytics

Fluxomedata

Prot IDs

Proteo
me
data
FluxI
Ds

Prot IDs
Flux
IDs

Proteome
data

SamplenameMeta data

Metabolome
data

MetI
Ds

Metabolome data

Met
IDs

Target aliases

IDs

Sample names
Meta data (replicates & etc.)

Target aliases

IDs

Fluxomedata

55

自動走行車の学習データ作成などへの応用例

INPUT

OUTPUTCANBUS
CameraOUTPUTGPSTechnology Platform
Autonomous AI Scientist

Autonomous AI

Integrated AI Module

Integrated AI

Basic AI Modules

Task Specific AI

Basic Functional Modules

Connectivity &Automation

Target discovery using Large-Scale Molecular Interaction
Map and Machine Learning based Docking Simulation
ERATO & AMED Project:
Discovery of host response modulating
factors for a novel influenza drug
development with application to avian flu
Collaboration with Prof. Kawaoka at theInstitute of Medical Science, theUniversity of Tokyo58

Network-based Compound Screening (NCS)
Case Study
439 host proteins from369 Compounds

Influenza Virus lifecycle
Protein Uniprot acc. Uniprot ID

PDB

ABL1

P00519

ABL1_HUMAN

2HZI

ABL2

P42684

ABL2_HUMAN

3GVU

Remarks

Abl kinase domain incomplex with PD180970 JIN
The crystal structure ofhuman ABL2 in complex withGLEEVEC STI

Watanabe et al., 2014

147$proteins

ML#based)Docking)simula4on
PD98059

PPI))
Network)Analysis

systemsDock1

304$proteins$$
3,321$edges$

Kun-Yi et al., 2016
Pepstatin A

PD98059

Pepstatin A

Pepstatin A! Based$ on$ docking$ score$ they$ were$clustered$ in$ diﬀerent$ groups,$
however,$ aMer$ including$ network$
property$ in$ the$ clustering$ algorithm$
both$ Pepsta8n9A$ and$ PD98059$ are$clustered$together$

Network-based
Compound Clustering

PD98059! For$ proof$ of$ concept,$ here$ we$
describe$ the$ example$ of$ compounds$
Pepsta8n9A$ (protease$ inhibitor)$ and$
PD98059$ (Map$ Kinase$ inhibitor)$marked$ by$ blue$ and$ red$ boxes$
respec8vely$$! Previous$ studies$ have$ shown$ that$these$ compound$ possess$ ac8vity$
against$viral$replica8on$in$human$cell$
cultures$ (Barber$ et$ al.,$ 2002$ and$Matarrese$et$al.,$2011).$

Docking-based
Compound ClusteringVery Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

Very Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

Literature-driven Approach
Influenza infection and replication network

Matsuoka Y. et al
BMC Syst. Biol. 2013

Large-Scale Knowledge Extraction from Text

BioNLP 2016

Key Case Studies
Text mining approach for BioMarker Discovery
Biomarker Knowledge Mining Pipeline

Biomarker Galaxy
Associate biomarkers with diseases

A domain specific word2vec Model

150,000 Articles on
Alzheimerʼs Disease
Over 15,000,000 words

Image + Text based Pathway
Reconstruction

PUBMED based hypothesis
generation system

PUBMED> 20 million
articles(a)(b)

Generate
hypotheses
(c)

(d)

Very Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

73

Network controllability Analysis

Critical Node
Critical Link

Network Controllability Analysis
Class-I Controllability Analysis: Full Controllability(Liu et al., Nature 2011)
FluMap(Statetransition
diagram)

Convert toBipartitegraph

Calculatemaximummatching with
Hopcroft-Karpalgorithm

Reflect
resultson FluMap*Identify
Critical Nodes
Critical Links

Computational Network Analysis(To identify and prioritize potential therapeutic targets)
Controllability:• Who is the major hub on the map?
• Interactions/paths between particular molecules?

75Computational Network Analyses(To identify and prioritize potential therapeutic targets)
Controllability:• Who is the major hub in the map?
• Interactions/paths between particular molecules?

76Computational Network Analyses(To identify and prioritize potential therapeutic targets)
Controllability:• Who is the major hub in the map?
• Interactions/paths between particular molecules?

1stst layer
layer

2nd layer

77

Very Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

The virus-interacting network and the virus subnetwork

Emily E. Ackerman et al. mBio 2018;
doi:10.1128/mBio.02002-18

Figure 2

Target Biological Processes

Potential Intervention Targets

Virus Associated Molecules

Cell Host & Microbe 2014 16, 795-805DOI: (10.1016/j.chom.2014.11.002)

82

Evaluating compounds that targets host factors

Very Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

Docking Simulation

Target protein• Posing• Scoring

Test compound

Hsin, Kamiyoshi, Asai
@ OISTPerformance Evaluation of Docking Simulation

Accuracy

Package A
Package B
Package C
Docking Pairs

ML-B
Prediction

ML-A
Re-scoring

Docking Software

Performance Evaluation of Docking Simulation

Performance Evaluation using Kinase Inhibitors
Selectivity = 1.0
Target Molecules Tested

Kd < 3uM Interactions

Selectivity = 0.1
Target Molecules Tested

Kd < 3uM Interactions
X-axis (selectivity score) = numberof interactions with Kd < 3 uM /number of kinases tested

K.Y.Shin, Ghosh, Kitano, PLoS One, 2013

A case study on a simple MAPK pathway
Bioassay contrast

Lapatinib

eHITS
Sunitinib

Bioassay contrast

GOLD
1
0

Our Method0
pKd/pKi

AutoDoc-VINA
Bioassay data is from Karaman, et al., Nature Immunology, 10 356-360, 2009Computational Screening à Experimental Validation
54,492
ActiveCompounds
In-house
Database

257,403,694
entries

66 Compounds
107
Proteintargets

JAK1

Watanabe et al., Cell Host & Microbe (2014),
http://dx.doi.org/10.1016/j.chom.2014.11.002

Results in Color Matrix

Proteins

Binding
Potential

Test Compounds

FluMap

CID 3795

Prof. Yoshihiro Kawaoka

18% proteins

JAK1:
5.69 pKd

Very Simplified Process of
System-Driven Drug Discovery
Disease Related
Molecular Interactions

TargetCandidates

TargetPrioritization

Multi-Omics Data

Toxicology
Clinical Studies

Pre-ClinicalStudies

Lead Compound
Identification and
Optimization

Uncovering Mechanism of Action of Maoto (麻黄湯) for Influenza
Complexity : 100s X 1000s X 1000s X …

27

Poly(I:C)
Influenza Virus(Polyinosinic-polycytidylic acid sodium salt)

RatNishi, A. et al., Deconstructing the traditional Japanese medicine “Kampo”: compounds,
metabolites and pharmacological profile of maoto, a remedy for flu-like symptoms,
npj Systems Biology and Applications, volume 3, Article number: 32 (2017)

Measuring Maoto Extract and Rat Plasma Metabolites

Number of CCDs
in Maoto extract
and rat plasma
CCD = Composition
determinedcompound

113 compounds
detected in Rat
plasma after Maoto
treatment

Hippuric acid
（馬尿酸）
Methylephedrine N-Oxide（メチルエフェドリン Nオキシ
ド）
Glycyrrhetinic acid
（グリチルレチン酸）

Nishi, A. et al., Deconstructing the traditional Japanese medicine “Kampo”: compounds,
metabolites and pharmacological profile of maoto, a remedy for flu-like symptoms,
npj Systems Biology and Applications, volume 3, Article number: 32 (2017)ラットにおける麻黄湯の生理学的影響

体温

運動量

体重

Host Physiology ProfilingState Change

Influenza Virus

Infection

DrugHost Physiology Profiling

Drug

麻黄湯

Maoto/Control
ω3-FA
COX

CYP

ω6-FA

LOX

47.6%&

46.7%&

Control

7.7%&
0.0%&

P/C

11.5%&

0&

M/C

11.5%&
6.7%&9.5%&

PM/P

PIC/Control
ω3-FA

Infection
Poly I:C

Joint research with Tsumura

PIC+Maoto/PIC
ω6-FA

ω3-FA

ω6-FA

Two Arms Robot for Lab

Crowd Robotics Consortium
100

Automation of Science Project

Nobody can develop an entire pipeline alone

Open Platform Open Collaboration

Nov 2011
Ghosh et al.

May 2011
Kitano, Ghosh, Matsuoka

Technology Platform
Autonomous AI Scientist

Autonomous AI

Integrated AI Module

Integrated AI

Basic AI Modules

Task Specific AI

Basic Functional Modules

Connectivity &Automation

Pre-Defined Sequences

Experiment and Analysis Pipeline
End-to-End Gadget Path

Operation Sequences &Parameter SearchProtocol Mark-up
Languages forboth experimentand analysis

GARUDA 2x
Automation and Recipes: Support for multi-device and crossdevice automation of processes and workflows through “Garuda
Recipes”

Confidential

GarudaとTaxilaを組み込んだ In-silico Experimental Platform
Recipe Conditions• Trait file• Ref DBetc.

KnowledgebaseInterpretation

Data Lake

Data
Selection

Data

Recipebuilder

ExecuteRecipeDashboard
Visualize Output

Build Pipeline

ReportOther
RecipesIn-silico Experiment
protocol txt

Recipe 1 steps:

Recipe 2 steps:

TaxilaPublic domain
data

Machine Learning• 次世代ナレッジ集積インテリジェン
ス解析プラットフォーム

Text Mining• Convert text to actionable insights:• Combine powerful
Experimental
aggregation, search,data
text mining, machine learning
Scientific publications
and AI technologies
多次元データと解析をつなぐクラウドベースのプラットフォーム
新しいデータや解析手法をダイナミックに構築可能なアーキテクチャ
対象分野情報と解析を提供し迅速な意思決定を支援

l

l

l

Deep Learning

A ”Living” system which grows andlearns by forming new connectionsTimecenter of learning :text to knowledge
Convert text to actionable insights: combine
powerful aggregation, search, text mining,machine learning and AI technologies.

Contextual
Aggregation

Text Analytics
and intelligence

Contextual
SearchCustom value
added services

Taxila provides automatic context-aware
aggregation and search of relevant information,
driven by AI-powered mining and analytics engine
for driving actionable insights with a intuitive userinterface.
Confidential

Industry and Research

Case Studies

Context specificknowledge basecreation andConcept/
Relationship
Identification

Summarization,
Visualization andinterpretation oflarge knowledge
bases

New Hypothesis
Generation

Real World
Evidence
Analysis

Drug
Repurposing

Biomarker
Discovery, Target
identification,
selection

Risk Assessmentin Genotoxicity
prediction andassessment

Text driven
network/ pathway
reconstruction

Key Opinion
LeaderIdentification

Sentiment
Analysis, Patentand Publication
Analytics, Social
Media Analytics

Trend Analysis/
Anomalydetection

Text driven network/ pathway reconstruction
Unstructured Data -> Structured representations -> Insights

RWE analysis for novel understanding of
Neurological diseases ( Unstructured Data -> Insights)

Text

(a)

Pathways(b)Tourette Syndrome,
(c)

(d)

Image + Text based Pathway Reconstruction

PUBMED scale hypothesis (link prediction)
hypothesis generation

PUBMED> 20 million
articles(a)Images(b)

Pathways
Related(c)work(d)Kembhavi, Aniruddha, et al. "A diagram is worth a dozen images." European
Conference on Computer Vision. Springer, Cham, 2016.

Related work
Sybrandt, Justin, Michael Shtutman, and Ilya
Safro. "Moliere: Automatic biomedical
hypothesis generation system." Proceedingsof the 23rd ACM SIGKDD International
Conference on Knowledge Discovery andData Mining. ACM, 2017.

Generate
hypotheses

Real World Evidence
Analysis

Comprehensive real world evidence collection, curation andanalysis to understand novel mechanism of specific neurologicalconditions to uncover new hypotheses.
Data source: Publications, Blogs, Discussion Forums, Clinical Trials,
Social Media
Focus: Tourette Syndrome, Depression, Autism and Schizophrenia

Joint work withmajor pharmaceutical company

Depression

Schizophernia

Tourette Syndrome

AutismLarge scale data collection
Real world
patient
discourse
Tourette
syndrome

Scientific
Publications

Clinical
TrialsData characteristics
100%
90%
80%
70%

Condition60%
50%

Depression

40%
30%
20%

Autism10%0%
tourette

Schizophernia

depression

Social chatter ( Discussion forums)

autismschizophernia

Publications

Clinical trials

The size of the bubble isrepresentative of thenumber of articles〜1,500,000 Articles Curated~ 1,500,000 Articles Curated

Text Analysis, NLP
and Machine Learning

Schizophernia

Novel insights
and new
hypothesis
generation

AutismTourette Syndrome

Depression

Outcome: Novel links between ʻTouretteʼ and SchizophreniaText driven
network/ pathway
reconstruction

To Structured Representations
from Unstructured text
Reconstructing structured pathway representations from
unstructured text

Ground-truth(human curated)
pathway(a)(c)

Automated
pathway
reconstructions

(b)

(d)

Future Reconstruction Challenge.
( Evaluate event extraction algorithms on a real data set )Spranger, M., Palaniappan, S., Ghosh, S.: Measuring the state of the art of automated pathway curation using graph algorithms - a case study of the mtor pathway. In: Proceedings of the 2016 Workshop
on Biomedical Natural Language Processing (BioNLP 2016), pp. 119–128. ACL
M. Spranger, S. Palaniappan, and S. Ghosh. Extracting biological pathway models from nlp event representations
BioNLP 2015, pages 42—51. ACL, 2015

Microbiome
LandscapeAnalysisWork
for due
Joint work
R&Ddiligence
Divisionanalysisof a Fortune
500 Company• Collect, curate and comprehend opportunities in science,technology and business dimensions in the areas of personal
omics with specific focus on microbiome• Deep analysis of state of the horizon in these areas conductedbased on the Taxila platform to identify strength, weaknesses,
opportunities and threats (SWOT) for business in this domain

Article Landscape, 360 view,
updated everyday
Total no. of focusedmicrobiome articles
Total no. of channels
Total no. of KOLs
identified
Total no. of Key
institutions identified>50000> 55> 5000> 1000Each bubble: a focus area
Size of the bubble: the number of articles

Galaxy view
Key scientific leaders
Galaxy ofknowledge
concepts from
All
graph
Alzheimerʼs Disease Papapers

Entire Hypothetical Body of Scientific Knowledge

False

Hypothesis
generation fromknowledge
Up-datingknowledge may
impact outcome ofprevious hypothesis

Experiments

Verified

Scientific
Knowledge in AI
systemAre newly verified
hypotheses consistent
with current
knowledge, or do theygenerateinconsistencies?

Know
ledge
Extraction
Portions of knowledge
believed to be correct
may in fact be false

Data added
to database

Hypotheses Generated

Some hypotheses
require experimental
verification

Experiments
may include
errors and noise

Integrating
Dark Data

Papers andDatabases

Papers and databases
contains errors,
inconsistencies, andeven fabrications
Kitano, H., AI Magazine, March 2016

Hypothesis Generation and Verification in AlphaGo
Learning Human Play

Human Expert
Positions

Exploring Unknown Play
Reinforcement
Learning
Policy Network

Supervised Learning
Policy Network

Deep Learning

Self-Play

Self-Play Data

Self-Play

Game status evaluation ispossible in the game of GO
Value network

AlphaGo
Human played gamesin the record

Possible moves basedon past human playedgames

All possible moves on GO

AlphaGo ZERO
Human played gamesin the record

AlphaGo
Possible moves
based on pasthuman played
games

AlphaGo Zero
Tabula rasa based
generation of games
All possible moves on GO

AI/ML

Modeling /Simulation

High-Precision
Experiments

Discovered knowledge

Human discoverable knowledge

Scientific truth

Discovered knowledge

Human discoverable knowledge

Knowledge that human may to be able togenerate and understandScientific truth

Data Acquisition and Generation
Existing Data＋
Data Acquisition＋
Virtual Data Generation（Populating Hypothesis Space）
Can we develop aproper evaluation
system?

Data Acquisition and Generation
Existing Data Discard biased data
Turn over to more＋accurate data
Data Acquisition＋
Virtual Data Generation（Populating Hypothesis Space）
Can we develop aproper evaluation
system?

Asking Right Questions
This may be critically important under resource
constraint situation. One cannot work on so many
issues, so focus on an important issue.

Eliminating a bottleneck
Build up ways to run super-fast hypothesis-testing cycle, sothat every questions can be answered quickly“Asking Right Questions” may not be that important anymore
Or
Human sense of “Right Questions” may be suboptimal

Redefining Scientific Discovery
Massive search and verification of
hypotheses space

How efficiently can we execute?
What are science specific constraints?
What is computational definition of “Serendipity”?

143

Is discovery path dependent?

One Universal AI Scientist

A population ofcharacteristic AI
Scientists

Process of Scientific Discovery

Serendipity
By Accident
Scientific Intuition

145

Implications•

Alternative forms of scientific discovery•

Alternative forms of intelligence•

Accelerating sciences at unprecedented speed•

Machines to evolve by itself?